SHapley Additive exPlanations (SHAP) Feature Importance.
The SHAP value is the average marginal contribution of a feature towards the model’s prediction across all possible combinations of features [1]}. It shows whether a feature value can increase a model’s prediction over a random baseline [2]}. However, the idea behind SHAP feature importance is that features with larger absolute SHAP values are more important than others. SHAP values can be calculated for any tree-based model. We calculate the SHAP values to rank features from the Random Forest model.
